Author(s)

 Lingxiang Wei ^1,*, Yuxuan Li ¹, Mingjun Liao ^1,2,*

Affiliation(s)

¹ School of Materials Science and Engineering, Yancheng Institute of Technology, Yancheng 224051, China;

² Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 710064, China.

Corresponding Author

Lingxiang Wei, email: weilx@ycit.edu.cn; 

Mingjun Liao, email: mjliao@126.com

ABSTRACT

In China, as in other countries, road traffic deaths are a burden for society. More than 58,000 people die in road crashes, and approximately 213,000 are injured annually in China. In 2018, there are 244,924 road traffic accidents all over China. To prevent traffic incidents, it is crucial to understand where and how they take place, the change trend in recent years and so on. The aim of this study is to make a change trend analysis in road traffic accidents concerning time and locations within macroscopic traffic accidents data from Yearbook of Road Traffic Accidents in China and National Bureau of Statistics of China using Smeed's Law. The result of this study shows that Smeed's Law clearly represents road traffic accidents prediction model in China. In addition, further studies will turn to analyze microcosmic causes of the traffic accidents.

KEYWORDS

Traffic engineering; road traffic accidents; Smeed's law; prediction model.

CITE THIS PAPER

Wei Lingxiang, Li Yuxuan, Liao Mingjun. Road traffic accidents prediction model in China. Eurasia Journal of Science and Technology. 2020, 2(1): 34-42.

REFERENCES

[1]Patel A, Krebs E, Andrade L, et al. The epidemiology of road traffic injury hotspots in Kigali, Rwanda from police data. BMC Public Health, 2016, 16(1), 697.

[2]Anjuman, Tahera, et al. Road traffic accident: A leading cause of the global burden of public health injuries and fatalities. Proc. Int. Conf. Mech. Eng, 2020,29-31

[3]Castillo-Manzano, J. I., Castro-Nu?o, M., López-Valpuesta, L., & Vassallo, F. V. An assessment of road traffic accidents in Spain: the role of tourism. Current Issues in Tourism, 2020, 23(6), 654-658.

[4]Ihueze, C. C., and U. O. Onwurah. Road traffic accidents prediction modelling: An analysis of Anambra State, Nigeria. Accid Anal Prev, 2018, 112, 21-29.

[5]Liang, Ci, et al. Developing accident prediction model for railway level crossings. Safety Science. 2018,101,48-59.

[6]Deublein, M, et al. Prediction of road accidents: A Bayesian hierarchical approach. Accident Analysis & Prevention.2013, 51 (4), 274-291.

[7]Deublein, Markus, M. Schubert, and B. T. Adey. Prediction of road accidents: comparison of two Bayesian methods. Structure & Infrastructure Engineering.2014, 10(11), 1394-1416.

[8]Yueh-Tzu Lu,Mototsugu Fukushige. Smeed’s law and the role of hospitals in modeling traffic accidents and fatalities in Japan. Asia-Pacific Journal of Regional Science.2019, 3(2), 319-332.

[9]Luca Persia, Roberto Gigli, Davide Shingo Usami. Smeed's law and expected road fatality reduction: An assessment of the Italian case. Journal of Safety Research.2015, 55, 121-133.

[10]Rolison J J, Regev S, Moutari S, et al. What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers’ opinions, and road accident records. Accident Analysis & Prevention, 2018, 115, 11-24.

[11]Potoglou, D., Carlucci, F., Cirà, A., & Restaino, M. Factors associated with urban non-fatal road-accident severity. International journal of injury control and safety promotion, 2018, 25(3), 303-310.

[12]Zhang, Z., He, Q., Gao, J., & Ni, M. A deep learning approach for detecting traffic accidents from social media data. Transportation research part C: emerging technologies, 2018, 86, 580-596.

[13]Yuan, Z., Zhou, X., & Yang, T. Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, 984-992.

[14]Ren, H., Song, Y., Wang, J., Hu, Y., & Lei, J. A deep learning approach to the citywide traffic accident risk prediction. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) IEEE, 2018, 3346-3351.

[15]Shi, X., Wong, Y. D., Li, M. Z. F., & Chai, C. Accident risk prediction based on driving behavior feature learning using CART and XGBoost. No. 18-06270, 2018.

[16]Gianfranco, F., Soddu, S., & Fadda, P. An accident prediction model for urban road networks. Journal of Transportation Safety & Security, 2018, 10(4), 387-405.

[17]La Torre, F., Meocci, M., Domenichini, L., Branzi, V., & Paliotto, A. Development of an accident prediction model for Italian freeways. Accident Analysis & Prevention, 2019, 124, 1-11.

[18]Soto, Borja Garc?-A De, B. T. Adey, and D. Fernando. A Process for the Development and Evaluation of Preliminary Construction Material Quantity Estimation Models Using Backward Elimination Regression and Neural Networks. Journal of Cost Analysis & Parametrics, 2014, 7(3), 180-218.

[19]R. J. Smeed, Some statistical aspects of road safety research. Journal of the Royal Statistical Society A. P. 1949, 112(1), 1-34.

[20]Raj V. Ponnaluri. Modeling road traffic fatalities in India: Smeed's law, time invariance and regional specificity. IATSS Research, 2012, 36(1), 75-82.

[21]Alghamdi A S. Road accidents in Saudi Arabia: a comparative and analytical study. Urban Transport & the Enviroment for Century II, 1996, 26, 23.

[22]Nasaruddin, Norashikin, et al. Fatality prediction model for motorcycle accidents in Malaysia. Statistics in Science, Business, and Engineering (ICSSBE), 2012 International Conference on. IEEE, 2012.

[23]Taamneh, Madhar, S. Alkheder, and S. Taamneh. Data-mining techniques for traffic accident modeling and prediction in the United Arab Emirates. Journal of Transportation Safety & Security, 2017, 9(2), 146-166.

[24]Seber G A F, Lee A J. Linear Regression Analysis, Second Edition, 2012.

[25]Abbondati, F., F. S. Capaldo, and R. Lamberti. Predicting driver speed behavior on tangent sections of low-volume roads. International Journal of Civil Engineering & Technology, 2017, 8(4), 1047-1060.

[26]Shen, Kun, and S. O. Management. Casualty Toll Prediction and Management Countermeasures for Road Traffic Accident in China. Safety & Environmental Engineering, 2017.

[27]Jiang, Bing Jiao, J. W. Shi, and Z. L. Chen. The Application of Cubic Exponential Smooth Model in Road Traffic Accidents Prediction. Value Engineering, 2017.